10 citations · 22 across the 8 of their papers we have counts for
12 papers
Model-based Deep Learning for Beam Prediction based on a Channel Chart
Taha Yassine, Baptiste Chatelier, Vincent Corlay +4
Channel charting builds a map of the radio environment in an unsupervised way. The obtained chart locations can be seen as low-dimensional compressed versions of channel state info…
Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
Taha Yassine, Luc Le Magoarou, Matthieu Crussière +1
Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna…
Channel charting based beamforming
Luc Le Magoarou, Taha Yassine, Stephane Paquelet +1
Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to…
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
Taha Yassine, Luc Le Magoarou, Stéphane Paquelet +1
Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper…
Deep learning for location based beamforming with NLOS channels
Luc Le Magoarou, Taha Yassine, Stéphane Paquelet +1
Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders. CSI acqui…
Online unsupervised deep unfolding for MIMO channel estimation
Luc Le Magoarou, Stéphane Paquelet
Channel estimation is a difficult problem in MIMO systems. Using a physical model allows to ease the problem, injecting a priori information based on the physics of propagation. Ho…